{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6abb6187",
   "metadata": {},
   "outputs": [],
   "source": [
    "@题目3 目标检测和语义分割"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5982ef30",
   "metadata": {},
   "source": [
    "# 构建 U-net 模型实现图像的语义分割"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9f5243",
   "metadata": {},
   "source": [
    "## 建立UNet类继承nn.Module，以CV中最常用的3x224x224作为输入尺寸，下采样提取特征，分别得到 64通道、128通道、256通道、512通道和1024通道的提取特征（类似编码过程）；\n",
    "## 在此基础上实现多层上采样（解码过程），逐步达到原始图像大小。（该过程中将通道数相同的下采样层特征和上采样层结果相连给到下个上采样层的输入）\n",
    "## 通过UNet输出3x分类数x224x224的特征图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4db8cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "class UNet(torch.nn.Module):\n",
    "    def __init__(self, all_kinds):\n",
    "        super(UNet, self).__init__()\n",
    "        self.stage_1 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(3, 32, 3, padding=1),\n",
    "            torch.nn.BatchNorm2d(32),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(32, 64, 3, padding=1),\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(64, 64, 3, padding=1),\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.stage_2 = torch.nn.Sequential(\n",
    "            torch.nn.MaxPool2d(2),\n",
    "            torch.nn.Conv2d(64, 128, 3, padding=1),\n",
    "            torch.nn.BatchNorm2d(128),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(128, 128, 3, padding=1),\n",
    "            torch.nn.BatchNorm2d(128),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.stage_3 = torch.nn.Sequential(\n",
    "            torch.nn.MaxPool2d(2),\n",
    "            torch.nn.Conv2d(128, 256, 3, padding=1),\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(256, 256, 3, padding=1),\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.stage_4 = torch.nn.Sequential(\n",
    "            torch.nn.MaxPool2d(2),\n",
    "            torch.nn.Conv2d(256, 512, 3, padding=1),\n",
    "            torch.nn.BatchNorm2d(512),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(512, 512, 3, padding=1),\n",
    "            torch.nn.BatchNorm2d(512),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.stage_5 = torch.nn.Sequential(\n",
    "            torch.nn.MaxPool2d(2),\n",
    "            torch.nn.Conv2d(512, 1024, 3, padding=1),\n",
    "            torch.nn.BatchNorm2d(1024),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(1024, 1024, 3, padding=1),\n",
    "            torch.nn.BatchNorm2d(1024),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.up_4 = torch.nn.Sequential(torch.nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1))\n",
    "        self.up_3 = torch.nn.Sequential(torch.nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1))\n",
    "        self.up_2 = torch.nn.Sequential(torch.nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1))\n",
    "        self.up_1 = torch.nn.Sequential(torch.nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1))\n",
    "        self.stage_up_4 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(1024, 512, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm2d(512),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm2d(512),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.stage_up_3 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(512, 256, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.stage_up_2 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(256, 128, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm2d(128),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm2d(128),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.stage_up_1 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(128, 64, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(64, 64, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.main = torch.nn.Sequential(torch.nn.Conv2d(64, all_kinds, kernel_size=3, padding=1))\n",
    "\n",
    "    def forward(self, x):\n",
    "        # 下采样\n",
    "        stage_1 = self.stage_1(x)\n",
    "        stage_2 = self.stage_2(stage_1)\n",
    "        stage_3 = self.stage_3(stage_2)\n",
    "        stage_4 = self.stage_4(stage_3)\n",
    "        stage_5 = self.stage_5(stage_4)\n",
    "        # 上采样\n",
    "        up_4 = self.up_4(stage_5)\n",
    "        up_4_conv = self.stage_up_4(torch.cat([up_4, stage_4], dim=1))\n",
    "\n",
    "        up_3 = self.up_3(up_4_conv)\n",
    "        up_3_conv = self.stage_up_3(torch.cat([up_3, stage_3], dim=1))\n",
    "\n",
    "        up_2 = self.up_2(up_3_conv)\n",
    "        up_2_conv = self.stage_up_2(torch.cat([up_2, stage_2], dim=1))\n",
    "\n",
    "        up_1 = self.up_1(up_2_conv)\n",
    "        up_1_conv = self.stage_up_1(torch.cat([up_1, stage_1], dim=1))\n",
    "\n",
    "        out = self.main(up_1_conv)\n",
    "\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c7f9ed6",
   "metadata": {},
   "source": [
    "# 数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27651524",
   "metadata": {},
   "source": [
    "## 数据集选择VOC2012数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cd016b72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.utils.data as data\n",
    "import albumentations as A\n",
    "import os\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from albumentations.pytorch.transforms import ToTensorV2\n",
    "class VDataset(data.Dataset):\n",
    "    def __init__(self,iii,img_dir,mask_dir):\n",
    "        self.trans=A.Compose([\n",
    "            A.Resize(224,224),\n",
    "            A.HorizontalFlip(),\n",
    "            A.VerticalFlip(),\n",
    "            A.Normalize(),\n",
    "            ToTensorV2()\n",
    "        ])\n",
    "        with open(iii,'r') as f:\n",
    "            self.ids=f.read().split()\n",
    "        self.img=[os.path.join(img_dir,image_id+'.jpg') for image_id in self.ids]\n",
    "        self.mask=[os.path.join(mask_dir,image_id+'.png') for image_id in self.ids]\n",
    "    def __getitem__(self, i):\n",
    "        # read data\n",
    "        image = np.array(Image.open(self.img[i]).convert('RGB'))\n",
    "        mask_s = np.array(Image.open(self.mask[i]).convert('RGB'))\n",
    "#         mask=np.zeros([mask_s.shape[0],mask_s.shape[1]])\n",
    "#         for i in range(mask_s.shape[0]):\n",
    "#             for j in range(mask_s.shape[1]):\n",
    "#                 mask[i,j]=VOC_COLORMAP.index([mask_s[i,j,0],mask_s[i,j,1],mask_s[i,j,2]])\n",
    "        image = self.trans(image=image,mask=mask)\n",
    "        return image['image'], image['mask'][:,:,0]\n",
    "    def __len__(self):\n",
    "        return len(self.ids)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c11d01f9",
   "metadata": {},
   "source": [
    "## 标签和类别对应表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6967df44",
   "metadata": {},
   "outputs": [],
   "source": [
    "iii='./data/seg/VOCdevkit/VOC2012/ImageSets/Segmentation/train.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d6b7e8e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.utils.data as data\n",
    "import albumentations as A\n",
    "import os\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from albumentations.pytorch.transforms import ToTensorV2\n",
    "class VDataset(data.Dataset):\n",
    "    def __init__(self,iii,img_dir,mask_dir):\n",
    "        self.trans=A.Compose([\n",
    "            A.Resize(224,224),\n",
    "            A.HorizontalFlip(),\n",
    "            A.VerticalFlip(),\n",
    "            A.Normalize(),\n",
    "            ToTensorV2()\n",
    "        ])\n",
    "        with open(iii,'r') as f:\n",
    "            self.ids=f.read().split()\n",
    "        self.img=[os.path.join(img_dir,image_id+'.jpg') for image_id in self.ids]\n",
    "        self.mask=[os.path.join(mask_dir,image_id+'.png') for image_id in self.ids]\n",
    "    def __getitem__(self, i):\n",
    "        # read data\n",
    "        image = np.array(Image.open(self.img[i]).convert('RGB'))\n",
    "        mask_s = np.array(Image.open(self.mask[i]).convert('RGB'))\n",
    "#         mask=np.zeros([mask_s.shape[0],mask_s.shape[1]])\n",
    "#         for i in range(mask_s.shape[0]):\n",
    "#             for j in range(mask_s.shape[1]):\n",
    "#                 mask[i,j]=VOC_COLORMAP.index([mask_s[i,j,0],mask_s[i,j,1],mask_s[i,j,2]])\n",
    "        image = self.trans(image=image,mask=mask)\n",
    "        return image['image'], image['mask'][:,:,0]\n",
    "    def __len__(self):\n",
    "        return len(self.ids)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4a821a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],\n",
    "                [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],\n",
    "                [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],\n",
    "                [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],\n",
    "                [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],\n",
    "                [0, 64, 128],[224,224,192]]\n",
    "VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat',\n",
    "               'bottle', 'bus', 'car', 'cat', 'chair', 'cow',\n",
    "               'diningtable', 'dog', 'horse', 'motorbike', 'person',\n",
    "               'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor','border']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b145dc9a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(VOC_CLASSES)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7740383",
   "metadata": {},
   "source": [
    "## 读入图像，设置batch_size为16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b00188d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = r'./data/seg/VOCdevkit/VOC2012/' \n",
    "id_train='./data/seg/VOCdevkit/VOC2012/ImageSets/Segmentation/train.txt'\n",
    "id_valid='./data/seg/VOCdevkit/VOC2012/ImageSets/Segmentation/val.txt'\n",
    "x_train_dir = os.path.join(DATA_DIR, 'JPEGImages')\n",
    "y_train_dir = os.path.join(DATA_DIR, 'SS')\n",
    "x_valid_dir = os.path.join(DATA_DIR, 'JPEGImages')\n",
    "y_valid_dir = os.path.join(DATA_DIR, 'SS')\n",
    "\n",
    "train_dataset = VDataset(\n",
    "    id_train,\n",
    "    x_train_dir, \n",
    "    y_train_dir, \n",
    ")\n",
    "val_dataset = VDataset(\n",
    "    id_valid,\n",
    "    x_valid_dir, \n",
    "    y_valid_dir, \n",
    ")\n",
    "\n",
    "batch_size=16\n",
    "train_loader = data.DataLoader(train_dataset, batch_size=16, shuffle=True,drop_last=True)\n",
    "val_loader = data.DataLoader(val_dataset, batch_size=16, shuffle=True,drop_last=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9d79df03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 3, 224, 224])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i, (x,y) in enumerate(train_loader):\n",
    "    break\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "686826d3",
   "metadata": {},
   "source": [
    "# 训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa254a2a",
   "metadata": {},
   "source": [
    "## 实例化UNet，类别数为22（21+border）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "55aadde1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = UNet(22).cuda()\n",
    "#model.load_state_dict(torch.load(r\"./data/checkpoints/Unet_75.pth\"), strict=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fefaaca",
   "metadata": {},
   "source": [
    "## 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a51c992",
   "metadata": {},
   "outputs": [],
   "source": [
    "from d2l import torch as d2l\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "#损失函数选用多分类交叉熵损失函数\n",
    "lossf = torch.nn.CrossEntropyLoss(ignore_index=21)\n",
    "#选用adam优化器来训练\n",
    "optimizer = torch.optim.SGD(model.parameters(),lr=0.1)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.1, last_epoch=-1)\n",
    " \n",
    "#训练50轮\n",
    "epochs_num = 50\n",
    "def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,scheduler,\n",
    "               devices=d2l.try_all_gpus()):\n",
    "    timer, num_batches = d2l.Timer(), len(train_iter)\n",
    "    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],\n",
    "                            legend=['train loss', 'train acc', 'test acc'])\n",
    "    net = torch.nn.DataParallel(net, device_ids=devices).to(devices[0])\n",
    "    \n",
    "    loss_list = []\n",
    "    train_acc_list = []\n",
    "    test_acc_list = []\n",
    "    epochs_list = []\n",
    "    time_list = []\n",
    "    for epoch in range(num_epochs):\n",
    "        # Sum of training loss, sum of training accuracy, no. of examples,\n",
    "        # no. of predictions\n",
    "        metric = d2l.Accumulator(4)\n",
    "        for i, (features, labels) in enumerate(train_iter):\n",
    "            timer.start()\n",
    "            l, acc = d2l.train_batch_ch13(\n",
    "                net, features, labels.long(), loss, trainer, devices)\n",
    "            metric.add(l, acc, labels.shape[0], labels.numel())\n",
    "            timer.stop()\n",
    "            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:\n",
    "                animator.add(epoch + (i + 1) / num_batches,\n",
    "                             (metric[0] / metric[2], metric[1] / metric[3],\n",
    "                              None))\n",
    "        test_acc = d2l.evaluate_accuracy_gpu(net, test_iter)\n",
    "        animator.add(epoch + 1, (None, None, test_acc))\n",
    "        scheduler.step()\n",
    "#         print(f'loss {metric[0] / metric[2]:.3f}, train acc '\n",
    "#               f'{metric[1] / metric[3]:.3f}, test acc {test_acc:.3f}')\n",
    "#         print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on '\n",
    "#               f'{str(devices)}')\n",
    "        print(f\"epoch {epoch} --- loss {metric[0] / metric[2]:.3f} ---  train acc {metric[1] / metric[3]:.3f} --- test acc {test_acc:.3f} --- cost time {timer.sum()}\")\n",
    "        \n",
    "        #---------保存训练数据---------------\n",
    "        df = pd.DataFrame()\n",
    "        loss_list.append(metric[0] / metric[2])\n",
    "        train_acc_list.append(metric[1] / metric[3])\n",
    "        test_acc_list.append(test_acc)\n",
    "        epochs_list.append(epoch)\n",
    "        time_list.append(timer.sum())\n",
    "        \n",
    "        df['epoch'] = epochs_list\n",
    "        df['loss'] = loss_list\n",
    "        df['train_acc'] = train_acc_list\n",
    "        df['test_acc'] = test_acc_list\n",
    "        df['time'] = time_list\n",
    "        df.to_excel(\"./data/Unet_camvid.xlsx\")\n",
    "        #----------------保存模型-------------------\n",
    "        if np.mod(epoch+1, 5) == 0:\n",
    "            torch.save(model.state_dict(), f'./data/checkpoints/4Unet_{epoch+1}.pth')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78483788",
   "metadata": {},
   "source": [
    "## 训练过程，绘制loss曲线图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7f6ab354",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 49 --- loss 0.008 ---  train acc 0.888 --- test acc 0.855 --- cost time 3542.1590569019318\n"
     ]
    },
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"238.965625pt\" height=\"184.455469pt\" viewBox=\"0 0 238.965625 184.455469\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
       " <metadata>\n",
       "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2022-11-29T20:01:32.983401</dc:date>\n",
       "    <dc:format>image/svg+xml</dc:format>\n",
       "    <dc:creator>\n",
       "     <cc:Agent>\n",
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      "text/plain": [
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   ],
   "source": [
    "train_ch13(model, train_loader, val_loader, lossf, optimizer, epochs_num,scheduler)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53eed874",
   "metadata": {},
   "source": [
    "## 预览语义分割效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cf61902e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "120c34ba",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "op=Image.fromarray(np.array(x.reshape(375,-1,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5d70b70b",
   "metadata": {},
   "outputs": [],
   "source": [
    "lp=torch.zeros([3,224,224])\n",
    "for i in range(224):\n",
    "    for j in range(224):\n",
    "        i_1=torch.argmax(pp[:,i,j])\n",
    "        lp[0,i,j]=Cam_COLORMAP[i_1][0]\n",
    "        lp[1,i,j]=Cam_COLORMAP[i_1][1]\n",
    "        lp[2,i,j]=Cam_COLORMAP[i_1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8380a019",
   "metadata": {},
   "outputs": [],
   "source": [
    "ll=torch.zeros([3,224,224])\n",
    "for i in range(224):\n",
    "    for j in range(224):\n",
    "        i_1=y[0,i,j]\n",
    "        ll[0,i,j]=Cam_COLORMAP[i_1][0]\n",
    "        ll[1,i,j]=Cam_COLORMAP[i_1][1]\n",
    "        ll[2,i,j]=Cam_COLORMAP[i_1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e7075d5a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 224, 224])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8e5aa546",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe22e280c40>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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     "output_type": "display_data"
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   ],
   "source": [
    "plt.imshow(np.array(ll.reshape(224,224,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a5943df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a364e5c4",
   "metadata": {},
   "source": [
    "# 切换数据集，使用CamVid数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c971419",
   "metadata": {},
   "source": [
    "## Camvid数据集类别映射表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7f1b0ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "Cam_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],\n",
    "                [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],\n",
    "                [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],\n",
    "                [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],\n",
    "                [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],\n",
    "                [0, 64, 128], [0, 32, 128],[0, 16, 128],[0, 64, 64],[0, 64, 32],\n",
    "                [0, 64, 16],[64, 64, 128],[0, 32, 16],[32,32,32],[16,16,16],[32,16,128],\n",
    "                [192,16,16],[32,32,196],[192,32,128], [25,15,125],[32,124,23],[111,222,113],\n",
    "               ]\n",
    " \n",
    "#32类\n",
    "Cam_CLASSES = ['Animal','Archway','Bicyclist','Bridge','Building','Car','CartLuggagePram','Child',\n",
    "               'Column_Pole','Fence','LaneMkgsDriv','LaneMkgsNonDriv','Misc_Text','MotorcycleScooter',\n",
    "               'OtherMoving','ParkingBlock','Pedestrian','Road','RoadShoulder','Sidewalk','SignSymbol',\n",
    "               'Sky', 'SUVPickupTruck','TrafficCone','TrafficLight', 'Train','Tree','Truck_Bus', 'Tunnel',\n",
    "               'VegetationMisc', 'Void','Wall']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b55b6cd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.utils.data as data\n",
    "import albumentations as A\n",
    "import os\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from albumentations.pytorch.transforms import ToTensorV2\n",
    "class CDataset(data.Dataset):\n",
    "    def __init__(self,img_dir,mask_dir):\n",
    "        self.trans=A.Compose([\n",
    "            A.Resize(224,224),\n",
    "            A.HorizontalFlip(),\n",
    "            A.VerticalFlip(),\n",
    "            A.Normalize(),\n",
    "            ToTensorV2()\n",
    "        ])\n",
    "\n",
    "        self.ids=os.listdir(img_dir)\n",
    "        self.img=[os.path.join(img_dir,image_id) for image_id in self.ids]\n",
    "        self.mask=[os.path.join(mask_dir,image_id) for image_id in self.ids]\n",
    "    def __getitem__(self, i):\n",
    "        # read data\n",
    "        image = np.array(Image.open(self.img[i]).convert('RGB'))\n",
    "        mask = np.array(Image.open(self.mask[i]).convert('RGB'))\n",
    "        image = self.trans(image=image,mask=mask)\n",
    "        return image['image'], image['mask'][:,:,0]\n",
    "    def __len__(self):\n",
    "        return len(self.ids)\n",
    "DATA_DIR = r'./data/seg/a/' \n",
    "x_train_dir = os.path.join(DATA_DIR, 'train')\n",
    "y_train_dir = os.path.join(DATA_DIR, 'trainannot')\n",
    "x_valid_dir = os.path.join(DATA_DIR, 'val')\n",
    "y_valid_dir = os.path.join(DATA_DIR, 'valannot')\n",
    "\n",
    "train_dataset = CDataset(\n",
    "    x_train_dir, \n",
    "    y_train_dir, \n",
    ")\n",
    "val_dataset = CDataset(\n",
    "    x_valid_dir, \n",
    "    y_valid_dir, \n",
    ")\n",
    "\n",
    "batch_size=16\n",
    "train_loader = data.DataLoader(train_dataset, batch_size=16, shuffle=True,drop_last=True)\n",
    "val_loader = data.DataLoader(val_dataset, batch_size=16, shuffle=True,drop_last=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7eb7a85",
   "metadata": {},
   "source": [
    "## 实例化UNet模型，类别数为32+1，多添加的是border类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d5755ed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = UNet(len(Cam_CLASSES)+1).cuda()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c971671b",
   "metadata": {},
   "source": [
    "## 省略训练过程，保存到了./data/checkpoints/1Unet_{epoch+1}.pth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd114a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "epoch 49 --- loss 0.018 ---  train acc 0.906 --- test acc 0.884 --- cost time 869.4878504276276\n"
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     "metadata": {
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    }
   ],
   "source": [
    "train_ch13(model, train_loader, val_loader, lossf, optimizer, epochs_num,scheduler)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24bfadbc",
   "metadata": {},
   "source": [
    "# VOC数据集预处理部分"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced6afc6",
   "metadata": {},
   "source": [
    "## VOC2012数据集中的mask标签是RGB通道的，为了方便训练过程中dataloader可以快速读取x和y值，此处对mask进行预先处理，将类别直接映射到像素上，即产生的新图像为RGB三通道数值相同且均为对应类别值的同尺寸图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c53ee64e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "ids=os.listdir('./data/seg/VOCdevkit/VOC2012/SegmentationClass/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "55d8675f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_s = np.array(Image.open('./data/seg/VOCdevkit/VOC2012/SegmentationClass/2007_000032.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "062008dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "ids=os.listdir('./data/seg/VOCdevkit/VOC2012/SegmentationClass/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0ef0a7ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(332, 500, 3)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6217e358",
   "metadata": {},
   "source": [
    "## 循环整理，总共有2900多张图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "644b8af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for ii in ids:\n",
    "    mask_s = np.array(Image.open('./data/seg/VOCdevkit/VOC2012/SegmentationClass/'+ii).convert('RGB'))\n",
    "    mask=np.zeros([mask_s.shape[0],mask_s.shape[1],3],dtype=np.uint8)\n",
    "    for i in range(mask_s.shape[0]):\n",
    "        for j in range(mask_s.shape[1]):\n",
    "            mask[i,j,:]=VOC_COLORMAP.index([mask_s[i,j,0],mask_s[i,j,1],mask_s[i,j,2]])\n",
    "    op=Image.fromarray(mask)\n",
    "    op.save('./data/seg/VOCdevkit/VOC2012/SS/'+ii)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7453600f",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir ./data/seg/VOCdevkit/VOC2012/SS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "768dc3ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(21., dtype=torch.float64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "02d3277d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(281, 500, 3)\n"
     ]
    }
   ],
   "source": [
    "x,y=train_dataset.__getitem__(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b14dbd2b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "46e3688d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1456 examples\n",
      "read 1436 examples\n"
     ]
    }
   ],
   "source": [
    "# import numpy as np\n",
    "# import os\n",
    "# from PIL import Image\n",
    "# def voc_colormap2label():\n",
    "#     \"\"\"构建从RGB到VOC类别索引的映射\"\"\"\n",
    "#     colormap2label = torch.zeros(256 ** 3, dtype=torch.long)\n",
    "#     for i, colormap in enumerate(VOC_COLORMAP):\n",
    "#         colormap2label[\n",
    "#             (colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i\n",
    "#     return colormap2label\n",
    "\n",
    "# def voc_label_indices(colormap, colormap2label):\n",
    "#     \"\"\"将VOC标签中的RGB值映射到它们的类别索引\"\"\"\n",
    "#     colormap = colormap.permute(1, 2, 0).numpy().astype('int32')\n",
    "#     idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256\n",
    "#            + colormap[:, :, 2])\n",
    "#     return colormap2label[idx]\n",
    "# def voc_rand_crop(feature, label, height, width):\n",
    "#     \"\"\"随机裁剪特征和标签图像\"\"\"\n",
    "#     rect = torchvision.transforms.RandomCrop.get_params(\n",
    "#         feature, (height, width))\n",
    "#     feature = torchvision.transforms.functional.crop(feature, *rect)\n",
    "#     label = torchvision.transforms.functional.crop(label, *rect)\n",
    "#     return feature, label\n",
    "# class VOCSegDataset(torch.utils.data.Dataset):\n",
    "#     \"\"\"一个用于加载VOC数据集的自定义数据集\"\"\"\n",
    "\n",
    "#     def __init__(self, is_train, crop_size, voc_dir):\n",
    "#         self.transform = torchvision.transforms.Normalize(\n",
    "#             mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "#         self.crop_size = crop_size\n",
    "#         features, labels = read_voc_images(voc_dir, is_train=is_train)\n",
    "#         self.features = [self.normalize_image(feature)\n",
    "#                          for feature in self.filter(features)]\n",
    "#         self.labels = self.filter(labels)\n",
    "#         self.colormap2label = voc_colormap2label()\n",
    "#         print('read ' + str(len(self.features)) + ' examples')\n",
    "\n",
    "#     def normalize_image(self, img):\n",
    "#         return self.transform(img.float() / 255)\n",
    "\n",
    "#     def filter(self, imgs):\n",
    "#         return [img for img in imgs if (\n",
    "#             img.shape[1] >= self.crop_size[0] and\n",
    "#             img.shape[2] >= self.crop_size[1])]\n",
    "\n",
    "#     def __getitem__(self, idx):\n",
    "#         feature, label = voc_rand_crop(self.features[idx], self.labels[idx],\n",
    "#                                        *self.crop_size)\n",
    "#         return (feature, voc_label_indices(label, self.colormap2label))\n",
    "\n",
    "#     def __len__(self):\n",
    "#         return len(self.features)\n",
    "# voc_dir='./data/seg/VOCdevkit/VOC2012/'\n",
    "# crop_size = (224, 224)\n",
    "# voc_train = VOCSegDataset(True, crop_size, voc_dir)\n",
    "# voc_test = VOCSegDataset(False, crop_size, voc_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "357013ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# batch_size = 16\n",
    "# train_iter = torch.utils.data.DataLoader(voc_train, batch_size, shuffle=True,drop_last=True)\n",
    "# test_iter = torch.utils.data.DataLoader(voc_test, batch_size, shuffle=True,drop_last=True)\n",
    "# dev=torch.device('cuda:0')\n",
    "# net=UNet(len(VOC_CLASSES)).to(dev)\n",
    "# loss = torch.nn.NLLLoss()\n",
    "# #选用adam优化器来训练\n",
    "# optimizer = torch.optim.Adam(net.parameters(),lr=0.001)\n",
    "# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.1, last_epoch=-1)"
   ]
  }
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